The attention mechanism is recognized as an effective technique to improve CNNs' performance. It imitates the human visual system, that is, focusing on the area of interest instead of the panorama. Therefore, the attention mechanism makes the network inclined to learn more valuable information, rather than useless noise. SE-Net [1]} is the most typical attention mechanism. It uses the mean value of each channel as global contextual information and integrates its embedding into the input, then flows this global knowledge into the network, so that the network can be aware of this knowledge. On the basis of SE-Net, various improvements have been made. For example, ECA-Net [2]} proposed an adaptive convolution kernel method to achieve local interactions, thereby significantly reducing model complexity. CBAM [3]} and BAM [4]} add additional information about the spatial dimension. Different from extracting global contextual information, SPA-Net [5]} and GE-Net [6]} extract local contextual information to make contextual information more general. In this paper, we consider both local and global contextual information, and recalibrate the input based on the correlation between the local and global information.
